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共 2833 条记录，以下是221-230 订阅

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Proceedings - 2023 6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

Proceedings - 2023 6th International Conference of Women in ...

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6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

ISBN: (纸本)9781665477239

the proceedings contain 40 papers. the topics discussed include: data science approach for climate change in Saudi Arabia: trend analysis;visualization approach to forecasting retail business;text mining to analyze mammogram screening results for breast cancer patients in Saudi Arabia;implementation of an effective framework in merging cybersecurity and software engineering;alarm rationalization for cybersecurity monitoring;a comparative assessment of accomplishment of sustainable development goals in tropical region;properties and future of the skew Kalman filters;web scraping for data analytics: a beautiful soup implementation;and a machine learning approach to identifying facial masks in real time.

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Performance Comparison of Different machine learning Classifiers for Diabetes Prediction 6th

Performance Comparison of Different Machine Learning Classif...

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Ghosh, Dipayan Ganguly, Abhik Chakraborty, Rounak Singh, Pawan Kumar Li, Aimin Department of Information Technology Jadavpur University West Bengal Kolkata India Shinawatra University Pathum Thani Thailand School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9789819780891

this paper presents a thorough evaluation of machine learning algorithms for assessing the risk of having diabetes with the help of the Pima Indian Diabetes dataset. In view of the global diabetes epidemic, timely and precise risk assessment is imperative. Our study involves an in-depth exploration of the data, uncovering a robust correlation between glucose levels and the likelihood of diabetes. We deploy a diverse set of nine machine learning models, encompassing logistic regression, decision trees, random forests, AdaBoost, support vector machine, K-nearest neighbors, Naive Bayes, XGBoost, and an artificial neural network approach. the results illuminate the strengths and weaknesses of each model, providing valuable insights for potential clinical applications. the logistic regression approach, in particular, showcases its ability to capture intricate patterns within the dataset, underscoring its effectiveness in diabetes risk assessment. In addition to traditional classifiers, we introduce an ensemble model that combines the strengths of five best performing classifiers. these findings not only improve the accuracy of diabetes risk assessment but also establish a benchmark for future research in medical diagnosis. Identifying the most effective model assists healthcare practitioners in early intervention and tailored treatment strategies. this research advances the field of healthcare analytics, facilitating more informed decisions in diabetes prevention and management. the integration of a robust ensemble model approach broadens the scope of potential applications, marking a significant contribution to the field. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Keyword Extraction-Based Audio and Video recognition Technology in Mobile Office Environment 6

Keyword Extraction-Based Audio and Video Recognition Technol...

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6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Lu, Teng Cuii, Zhaowe Li, Qingbo Li, Xiaomeng Yuan, Zhou Xia, Ang State Grid Information & Telecommunication Co. Ltd. Beijing China

ISBN: (纸本)9781837242672

this paper explores the integration of keyword extraction techniques into audio and video recognition systems designed for mobile office environments. As the prevalence of mobile devices reshapes traditional workspaces, the demand for advanced technologies that can efficiently process and analyze audio and video data in real time has increased significantly. this research examines the unique challenges presented by dynamic mobile contexts, such as fluctuating background noise, varying audio quality, and the mobility of devices, which complicate traditional recognition methods. To address these issues, a novel framework is proposed that combines both machine learning and deep learning techniques to enhance the accuracy and robustness of audio and video recognition in such environments. Experimental validation of the proposed model demonstrates a high accuracy of 92% in keyword detection, confirming its effectiveness in real-world applications. the results highlight the potential of this framework to not only improve recognition performance but also to create more adaptive, context-aware systems capable of optimizing user experience in mobile office settings. this study contributes to the development of smarter, more efficient recognition systems that are better suited to the evolving demands of modern workspaces. © the Institution of Engineering & Technology 2024.

关键词： Deep learning

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English Speech recognition Based on Deep machine learning Algorithm 6th

English Speech Recognition Based on Deep Machine Learning Al...

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6th international conference on Innovative Computing, IC 2023

作者： Du, Aiyan Qilu Institute of Technology Shandong Jinan250200 China

ISBN: (纸本)9789819922864

With the deepening of artificial intelligence technology in people's life, English speech recognition, as an indispensable technical means, has become the focus of attention. In the past decades, the acoustic model combined with Gaussian mixed model hidden Markov model (gmm-hmm) has occupied a leading position in the field of English speech recognition technology. However, with the English speech data becoming more and more large and complex, the shortcomings of the traditional model are gradually revealed. English speech recognition based on deep machine learning algorithm is a branch of artificial intelligence, which mainly studies human English speech understanding. It uses pattern recognition, statistical modeling and computer vision to achieve this goal. Deep neural network (DNN) has been used as the basis of many other types of algorithms;English voice to text system, using DNN to convert spoken language into text;Automatic transcription system, using DNN to recognize English speech, and then convert it into text;machine translation between different languages using DNN. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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Process-Aware Bayesian Networks for Sequential Event Log Queries 6

Process-Aware Bayesian Networks for Sequential Event Log Que...

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6th international conference on Process mining (ICPM)

作者： Rauch, Simon Zellner, Ludwig Seidl, thomas Freyt, Christian M. M. Ludwig Maximilians Univ Munchen Database Syst & Data Min Munich Germany Munich Ctr Machine Learning Munich Germany Fraunhofer IIS Ctr Appl Res Supply Chain Serv Nurnberg Germany

ISBN: (纸本)9798350365030

Business processes from many domains like manufacturing, healthcare, or business administration suffer from different amounts of uncertainty concerning the execution of individual activities and their order of occurrence. As long as a process is not entirely serial, i.e., there are no forks or decisions to be made along the process execution, we are - in the absence of exhaustive domain knowledge - confronted with the question whether and in what order activities should be executed or left out for a given case and a desired outcome. As the occurrence or non-occurrence of events has substantial implications regarding process key performance indicators like throughput times or scrap rate, there is ample need for assessing and modeling that process-inherent uncertainty. We propose a novel way of handling the uncertainty by leveraging the probabilistic mechanisms of Bayesian Networks to model processes from the structural and temporal information given in event log data and offer a comprehensive evaluation of uncertainty by modelling cases in their entirety. In a thorough analysis of well-established benchmark datasets, we show that our Process-aware Bayesian Network is capable of answering process queries concerned with any unknown process sequence regarding activities and/or attributes enhancing the explainability of processes. Our method can infer execution probabilities of activities at different stages and can query probabilities of certain process outcomes. the key benefit of the Process-aware Query System over existing approaches is the ability to deliver probabilistic, case-diagnostic information about the execution of activities via Bayesian inference.

关键词： Process Query Systems Bayesian Networks Predictive Process Monitoring

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Chinese Nested Named Entity recognition in the Medical Field Based on BiLSTM and Multi-Head Global Pointer 5

Chinese Nested Named Entity Recognition in the Medical Field...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Zhao, Xinke Yilahun, Hankiz Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9798350355925

the technology of named entity recognition has been gradually refined. However, current research is primarily focused on non-nested named entity recognition, with less attention devoted to nested named entity recognition (NNER). the challenge of accurately extracting nested entities from complex medical texts remains. To address the issue of low accuracy in identifying nested entities in medical named entity recognition, we propose a Chinese nested named entity recognition model (Multi-ABGP) based on adversarial training, bidirectional long short-term memory (BiLSTM) and multi-headed global pointer network. this model employs a BiLSTM network to extract bidirectional and long-distance text features, enhancing the ability of the global pointer network to extract features from longer text. Furthermore, adversarial training is employed to reduce the impact of noise on the model's performance and enhance its robustness. this enables the model to ignore irrelevant features introduced by noise and instead focus on entity-related features. Finally, a multi-head global pointer network is utilized to fuse the features of multiple global pointer networks. this reduces the erroneous prediction of a single pointer network and improves the overall performance of the model. the experimental results demonstrate that the proposed model achieves Micro-F1, Micro-P, and Micro-R values of 73.71%, 74.96%, and 72.50%, respectively, on the CMeEE-V2 dataset of Chinese nested named entity recognition in the medical specialty. this significantly improves the effectiveness of Chinese nested entity recognition in the medical field. And we also compare it with the large language model (LLM), the experimental results show that the model outperforms the ChatGLM in the nested entity recognition task. Additionally, it exhibits favorable results on the non-nested entity recognition dataset People's Daily. © 2024 IEEE.

关键词： data mining

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An Approach to Modules Similarity Definition Based on System Trace Analysis 6th

An Approach to Modules Similarity Definition Based on Syst...

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6th international conference on Software Testing, machine learning and Complex Process Analysis, TMPA 2021

作者： Samonenko, Ilya Voznesenskaya, Tamara Yavorskiy, Rostislav Higher School of Economics Moscow101000 Russia Tomsk Polytechnic University Tomsk634050 Russia

ISBN: (纸本)9783031504228

We study a distributed process organised as a sequence of somehow related modules. A token passes through different modules and leaves a digital trace in the log. Our goal is to design an optimal distance function between modules for clustering purposes, finding abnormal behaviour and predicting the digital trace of new coming tokens. © 2024, Springer Nature Switzerland AG.

关键词： data mining

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the 6th Workshop on e-eommerce and NLP (ECNLP 6) 23

The 6th Workshop on e-eommerce and NLP (ECNLP 6)

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29th ACM SIGKDD conference on Knowledge Discovery and data mining, KDD 2023

作者： Malmasi, Shervin Fetahu, Besnik Agichtein, Eugene Rokhlenko, Oleg Guy, Ido Ueffing, Nicola Kallumadi, Surya *** Inc. SeattleWA United States Emory University AtlantaGA United States Ben-Gurion University of the Negev Tel-Aviv Israel EBay Inc. Berlin Germany Lowe's Companies Inc. AtlantaGA United States

ISBN: (纸本)9798400701030

Natural Language Processing (NLP) technology plays a key role in e-commerce today, where this technology can be used for a range of tasks, such as improving search results, providing recommendations, and powering virtual assistants. the ECNLP workshop series focuses on NLP and machine learning methods for e-commerce, with a focus on applied and fundamental machine learning and NLP methods that can be leveraged in applied settings. the workshop aims to being together researchers from both industry and academia, with the goal of fostering greater knowledge sharing and collaboration between researchers and practitioners in this field. © 2023 Owner/Author.

关键词： machine learning

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Transformer Models for Predicting Bank Loan Defaults a Next-Generation Risk Management 6

Transformer Models for Predicting Bank Loan Defaults a Next-...

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6th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2024

作者： Kakadiya, Rushikesh Khan, Tarannum Diwan, Anjali Mahadeva, Rajesh Marwadi University Ce - Ai & Big Data Rajkot India Marwadi University Department of Ce Rajkot India Manipal Institute of Technology Manipal Academy of Higher Education Department of Cse Manipal India

ISBN: (纸本)9798331505790

Predicting bank loan defaults are crucial for the financial banking industry since it allows organizations to manage risk proactively so it make educated decision. While important, conventional method such as logistic regression and decision trees can face challenges when attempting to handle complex pattern and substantial relationships that are inherent in financial data. the Transformer design, which offers enormous promise in handling sequential data model, was made possible by recent advancement in natural language processing (NLP). In order to simplify the links between various financial variables, these work explores the use of transformers for bank loan default prediction, using its self-attention mechanisms. By paying attention to multiple input pieces at once, the Transformer model is able to catch subtleties and dependencies that are missed by traditional method. the Transformer based paradigm outperforms last approach in terms of performance and interpretability, as demonstrated by empirical finding. It offers valuable insights into how different financial factors are connected and how they influence the prediction of loan defaults. As a result, the Transformer becomes an essential tool in financial risk management and has the potential to change the way we predict loan defaults. Using this technology presents a strong and effective solution to the difficulties encountered with traditional methods, allowing for a deeper understanding of complex financial patterns that lead to more accurate predictions. © 2024 IEEE.

关键词： Prediction models

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Sugarcane Bud Detection Using YOLOv5 6th

Sugarcane Bud Detection Using YOLOv5

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Sindhe, P. Swathi Hegadi, Ravindra S. Department of Computer Science Central University of Karnataka Kadaganchi Karnataka Kalaburagi India

ISBN: (纸本)9783031530814

this paper addresses the labor-intensive and wasteful nature of the traditional sugarcane bud cutting method. To overcome these challenges, the proposed approach leverages YOLOv5 technology for sugarcane bud identification. the machine-learning model is trained with diverse data samples, enabling it to accurately distinguish between sugarcane buds and other elements in the images. the implementation yields the best of 79% accuracy in bud detection. By automating the process, the proposed method significantly reduces labor and time requirements while minimizing sugarcane wastage. this innovation presents promising implications for the sugarcane industry, as it streamlines bud identification and optimizes resource utilization. Furthermore, the adoption of YOLOv5 technology can potentially extend to other agricultural domains, offering opportunities for enhanced crop management and sustainable farming practices. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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